Applying explainable AI algorithms to healthcare

Saliency map explanation for an ECG exam that is predicted to be low-quality. Red highlights the part of the image most important to the model's prediction, while purple indicates the least important area. Ana Lucic, a PhD candidate at the Information Retrieval Lab (IRLab) of the Informatics Institute of UvA, has developed a framework for explaining predictions of machine learning models that could improve heart examinations for underserved communities. The work of Lucic is part of the subfield of AI, called explainable artificial intelligence (XAI). "We need explainable AI", says Lucic, "because machine learning models are often difficult to interpret. They have complex architectures and large numbers of parameters, so it's not clear how the input contributes to the output."